CN110555989A - Xgboost algorithm-based traffic prediction method - Google Patents
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Abstract
the invention discloses a traffic prediction method based on an Xgboost algorithm, which comprises the following steps: step S1: collecting traffic data, carrying out data normalization, and dividing the traffic data into training data and test data; step S2: performing model training on training data based on an Xgboost algorithm, and determining model parameters; step S3: inputting Xgboost model parameters and test data to predict traffic flow; step S4: and carrying out error evaluation on the prediction result of the Xgboost model, and reducing the prediction data for outputting. The Xgboost model improves the prediction precision, greatly reduces the prediction time, and has better prediction performance and generalization capability in the prediction of the traffic volume of the expressway.
Description
Technical Field
The invention relates to the technical fields of machine learning methods, traffic prediction and the like, in particular to a traffic prediction method based on an Xgboost algorithm.
Background
the traffic flow is an important index reflecting the traffic state of the highway, and the short-term traffic prediction is an important content of highway management and is a key direction of intelligent traffic. The highway system is a complex nonlinear system, and the study on short-term traffic volume is beneficial to the issuing of traffic information by management departments, the improvement of traffic induction effect and the improvement of the operating efficiency and stability of the highway system.
in the aspect of statistical characteristic research, a trend extrapolation method, linear regression, an invisible Markov prediction model, Kalman filtering and the like are provided. On the machine learning method level, the iterative estimation of the traffic volume is realized by mining the information implied by the historical data. Different models such as a support vector machine, an iterative decision tree, a random forest, a Bayesian network, a wavelet theory, an improved particle swarm optimization BP neural network and the like are applied to the prediction of traffic volume. In deep learning, a deep belief network is adopted to firstly perform feature learning extraction on data, and then a top-level SVM model is adopted to perform prediction.
the model applies the machine learning theory to the traffic prediction field and obtains better results. However, under limited calculation conditions, the models have more parameters and long model prediction time due to the fact that double or even more models are compounded, and the prediction accuracy is influenced.
Disclosure of Invention
Aiming at the problems, the invention provides a traffic prediction method based on an Xgboost algorithm, aiming at solving the problems of difficult parameter optimization, large computing resource consumption and poor prediction performance caused by composite model prediction in traffic prediction.
The above object of the present invention is achieved by at least one of the following technical solutions:
A traffic prediction method based on an Xgboost algorithm comprises the following steps:
Step S1: collecting traffic data, carrying out data normalization, and dividing the traffic data into training data and test data;
Step S2: performing model training on training data based on an Xgboost algorithm, and determining model parameters;
step S3: inputting Xgboost model parameters and test data to predict traffic flow;
Step S4: and carrying out error evaluation on the prediction result of the Xgboost model, and reducing the prediction data for outputting.
further, the step S1 specifically includes:
Collecting and counting running vehicle information by using a coil, and dividing the running vehicle information into a plurality of traffic volume data with different time intervals;
Normalizing data, and dividing the data into training data and test data according to a proportion, wherein the data normalization is standardized by min-max, and the formula is as follows:
And x is the normalized traffic data, wherein max is the maximum value of the sample data, and min is the minimum value of the sample data.
Further, the traffic data divided into a plurality of different time intervals specifically refers to traffic data divided into two different intervals of 30 minutes and 60 minutes.
further, in step S2, the Xgboost algorithm combines the prediction results of a series of weak learners into a strong learner, performs second-order taylor expansion on the loss function, combines the prediction term with the regularization term, adds second-order derivative information in the optimization process, simplifies the objective function to realize calculation resource optimization, and selects appropriate parameters by using a weak classifier integration algorithm, where the specific process includes:
step S21, operating training data by using Ridge Regression algorithm (Ridge Regression), and selecting an optimal alpha value;
step S22, operating training data by adopting a Random Forest algorithm (Random Forest) and selecting optimal parameters;
And step S23, adopting a fusion algorithm (Stacking) to take the advantages of the two models, extracting the optimal parameters and finishing model training.
Further, the step S3 specifically includes:
and inputting Xgboost model parameters, processing input data by adopting a time window step length parameter, inputting the traffic data of the previous n moments into the trained Xgboost model, and generating a traffic data prediction result of the next moment, namely the traffic of the n +1 moment.
further, the step S4 specifically includes:
adopting a trained Xgboost model to predict traffic volume of predicted data, carrying out error calculation on the predicted data and actual data, wherein the error calculation adopts Mean Square Error (MSE) and Root Mean Square Error (RMSE) as evaluation indexes, and reducing the predicted data to output:
mean square error:
Root Mean Square Error (RMSE):
In the formula: n is the number of data sets,Representing predictive data, yirepresenting the real data.
The invention provides a traffic prediction method based on an Xgboost algorithm, which comprises the following steps: carrying out normalization processing on data acquired by the coil, and dividing the data into training data and testing data; optimizing model parameters of the LSTM neural network by adopting a particle swarm algorithm; training an Xgboost model; and calling a prediction model to predict the test data, and evaluating a prediction error. The method utilizes the characteristics of integration of the Xgboost model with a weak learner and high operation speed, can obtain higher prediction precision, and has good applicability to different interval data samples.
Compared with the prior art, the invention has the beneficial effects that:
1. The CART tree is used at the bottom layer of the Xgboost model, and leaf nodes adopt numerical values, so that the efficient optimization of the algorithm is facilitated, and the running speed is increased;
2. the Xgboost model classification tree cutting points adopt an approximate value algorithm, and the operation speed is improved by an enumeration algorithm;
3. the Xgboost model expands the loss function to second-order conductibility, so that the optimal solution can be obtained more quickly;
3. The Xgboost model has good applicability to data samples of different intervals.
drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
figure 2 is a graph of the predicted mean square error in the different data for the four models.
fig. 3 is a predicted mean square heel for different data of the four models.
Detailed Description
The invention is further illustrated by the following examples, which are intended to facilitate the understanding of the invention, but are not intended to be limiting in any way.
A traffic prediction method based on Xgboost algorithm, the main flow of which is shown in figure 1, comprises the following steps:
step S1: and acquiring traffic data, performing data normalization preprocessing, and dividing the data into training data and testing data.
the traffic data is derived from vehicle information acquired by an urban highway line circle, the traffic information in a time period is acquired, data sample intervals can be set according to actual prediction requirements, and two interval sample data of 30 minutes and 60 minutes are adopted in the invention. Reading to obtain original traffic data, and normalizing the data by adopting a min-max standardization method:
and x is the normalized traffic data, wherein max is the maximum value of the sample data, and min is the minimum value of the sample data.
Step S2, based on the Xgboost algorithm, model training is carried out on training data, and model parameters are determined; the Xgboost combines the prediction results of a series of weak learners into a strong learner, performs second-order Taylor expansion on the loss function, combines the prediction term and the regularization term, adds second-order derivative information in the optimization process, simplifies the objective function and realizes calculation resource optimization. And selecting proper parameters by adopting a weak classifier integration algorithm.
the method specifically comprises the following steps:
Step S21, operating training data by using Ridge Regression algorithm (Ridge Regression), and selecting an optimal alpha value;
step S22, operating training data by adopting a Random Forest algorithm (Random Forest) and selecting optimal parameters;
S23, adopting a fusion algorithm (Stacking) to take the advantages of the two models, extracting optimal parameters and finishing model training;
step S3: inputting Xgboost model parameters and test data to predict traffic flow; the method specifically comprises the following steps: and inputting Xgboost model parameters, processing input data by adopting a time window step length parameter, inputting the traffic data of the previous n moments into the trained Xgboost model, and generating a traffic data prediction result of the next moment, namely the traffic of the n +1 moment.
Step S4, carrying out error evaluation on the Xgboost model prediction result, restoring the prediction data and outputting, wherein the method specifically comprises the following steps: carrying out traffic volume prediction on predicted data by adopting a trained Xgboost model, carrying out error calculation on the predicted data and actual data, wherein the error calculation adopts Mean Square Error (MSE) and Root Mean Square Error (RMSE) as evaluation indexes:
Mean square error:
Root Mean Square Error (RMSE):
in the formula: n is the number of data sets,representing predictive data, yirepresenting the real data.
the effectiveness of the invention can be further illustrated by the examples, the data of which do not limit the scope of application of the invention.
an experiment platform: the processor is Intel i5-6500, and the memory is 16.0 GB; the system is Windows10(64 bits); the program language version is python 3.6.
The experimental contents are as follows:
the data source of this embodiment is coil data of a road in Guangzhou city. The method is adopted to be traffic data every 5 minutes. The data volume of this embodiment is big, and the authenticity is high. The data are collected at intervals of 30 minutes and 60 minutes respectively after being sorted, and the data prediction requirements of management departments can be effectively guaranteed. And (4) carrying out normalization by adopting a dispersion standardization method, wherein the data in the first 8 days of the experiment is taken as a training set, and the data in the last 2 days is taken as a testing set.
obtaining Xgboost model parameters according to the training data, wherein the Xgboost model parameters are respectively as follows: learning _ rate is 0.1, n _ estimators is 100, max _ depth is 5, min _ child _ weight is 5, gamma is 0.1, reg _ alpha is 1, reg _ lambda is 1. These parameters were used as parameters for the Xgboost model for prediction of test data.
The experiment selects the model commonly used in journey time prediction as a control: random forest algorithm (RF), support vector machine algorithm (SVM), nearest neighbor algorithm (KNN), with the algorithm of the present invention (Xgboost) for comparison of prediction performance.
fig. 2 shows the predicted mean square error in different data of the four models, and fig. 3 shows the predicted mean square heel in different data of the four models. The travel time prediction performance pairs for the four models are shown in table 1.
TABLE 1 comparison of travel time prediction performance for the algorithm
In conclusion, the traffic prediction method based on the Xgboost algorithm can obtain better prediction performance, and improves the traffic prediction precision while reducing the complexity of the model, overfitting and the calculated amount. The method provided by the invention has the lowest error in two different interval data, and has good applicability.
The above is an example of the present invention, but the present invention is not limited to the above specific embodiments, and when the function of the modification made according to the technical scheme of the present invention is not beyond the scope of the technical scheme of the present method, the modification should be regarded as the disclosure of the present invention.
Claims (6)
1. a traffic prediction method based on Xgboost algorithm is characterized by comprising the following steps:
Step S1: collecting traffic data, carrying out data normalization, and dividing the traffic data into training data and test data;
step S2: performing model training on training data based on an Xgboost algorithm, and determining model parameters;
step S3: inputting Xgboost model parameters and test data to predict traffic flow;
Step S4: and carrying out error evaluation on the prediction result of the Xgboost model, and reducing the prediction data for outputting.
2. The method for predicting the traffic volume based on the Xgboost algorithm as claimed in claim 1, wherein said step S1 specifically includes:
collecting and counting running vehicle information by using a coil, and dividing the running vehicle information into a plurality of traffic volume data with different time intervals;
normalizing data, and dividing the data into training data and test data according to a proportion, wherein the data normalization is standardized by min-max, and the formula is as follows:
And x is the normalized traffic data, wherein max is the maximum value of the sample data, and min is the minimum value of the sample data.
3. the Xgboost algorithm-based traffic prediction method according to claim 2, wherein the traffic data divided into different time intervals is specifically traffic data divided into two different intervals of 30 minutes and 60 minutes.
4. The method for predicting traffic volume based on Xgboost algorithm as claimed in claim 1, wherein in said step S2, the Xgboost algorithm combines the prediction results of a series of weak learners into a strong learner, performs second order taylor expansion on the loss function, combines the prediction term and the regularization term, adds the second order derivative information in the optimization process, simplifies the objective function to realize the optimization of computational resources, adopts the weak classifier integration algorithm to select suitable parameters, and the specific process includes:
step S21, operating training data by using a ridge regression algorithm, and selecting an optimal alpha value;
s22, running training data by adopting a random forest algorithm, and selecting optimal parameters;
and S23, adopting a fusion algorithm to take the advantages of the two models, extracting the optimal parameters and finishing the model training.
5. the method for predicting the traffic volume based on the Xgboost algorithm as claimed in claim 1, wherein said step S3 specifically includes:
And inputting Xgboost model parameters, processing input data by adopting a time window step length parameter, inputting the traffic data of the previous n moments into the trained Xgboost model, and generating a traffic data prediction result of the next moment, namely the traffic of the n +1 moment.
6. The method for predicting the traffic volume based on the Xgboost algorithm as claimed in claim 1, wherein said step S4 specifically includes:
Adopting a trained Xgboost model to predict traffic volume of the predicted data, carrying out error calculation on the predicted data and actual data, adopting a mean square error and a root mean square error as evaluation indexes in the error calculation, restoring the predicted data and outputting:
mean square error:
root mean square error:
In the formula: n is the number of data sets,representing predictive data, yirepresenting the real data.
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